planning-with-files-zht
用於多步驟 AI 代理工作的持久化檔案規劃。將 task_plan.md、findings.md 與 progress.md 保存在磁碟上，生命週期鉤子會注入選定的專案規劃內容。自動恢復只讀取專案規劃檔案；只有明確執行 session-catchup.py --metadata 才會檢查本機同一專案的代理工作階段中繼資料，--replay 則會輸出有界且以 nonce 框定的摘錄。選用的閘門模式只會在主機支援時要求繼續，而且絕不執行 Markdown 中宣告的命令。此技能沒有網路上傳路徑。適用於研究或需要超過 5 次工具呼叫的工作。觸發詞：任務規劃、專案計畫、制定計畫、分解任務、多步驟規劃、進度追蹤、檔案規劃、幫我規劃、拆解專案
Before you install
- Last update
- Files in folder
- 26
- Scripts
- Contains scripts. Review them before running.
- SKILL.md size
- 13 KB (about 3,294 tokens of context each time it loads)
- License
- MIT
- Source on GitHub
- OthmanAdi/planning-with-files
Skills are plain text instructions the agent follows, sometimes with scripts. Check the source, prefer repositories with many installs and stars, and read any script it ships.
Install
npx skills add OthmanAdi/planning-with-files@planning-with-files-zhtRuns in a terminal with Node.js 18 or newer. Add -y to skip the prompts and -g to install for every project instead of this one.
Install for your agent
npx skills add OthmanAdi/planning-with-files@planning-with-files-zht --agent claude-code -y- Project install writes to
.claude/skills/planning-with-files-zht- Global install writes to
~/.claude/skills/planning-with-files-zht
Start a new session afterwards so the agent picks the skill up. In Claude Code you can also call it directly with /planning-with-files-zht.
What it does
檔案規劃系統 像 Manus 一樣工作：用持久化的 Markdown 檔案作為你的「磁碟工作記憶」。 第一步：恢復專案狀態 繼續之前，先解析此任務所屬的計畫目錄： 1. 使用已安裝的 scripts/resolve-plan-dir.sh（或 .ps1），配合主機的 PLANID 與 PWFPLANROOT，從這一個選定目錄讀取 taskplan.md、progress.md 和 findings.md。 2. 若明確選擇器被拒絕，或工作階段隔離已啟用且有多個計畫卻沒有 PLANID，請修正釘選，不要退回另一項任務。只有沒有適用的選擇器或具名計畫時，才使用專案根目錄的舊檔案。 3. 執行 git diff --stat，查看尚未記錄的程式碼變更。 下列所有規劃檔案名稱都指向這個選定目錄。平行任務時，請在啟動每個主機前釘選它，或使用獨立 worktree；在子程序中匯出變數不會改變主機環境。一位協調者擁有共享計畫與摘要，工作者使用指派的檔案或帳本。 自動恢復到此為止。未指定模式的 session-catchup.py 與生命週期鉤子不會檢查代理工作階段儲存區。只有在使用者明確要求查閱本機工作階段歷史時，才能選擇下列模式： 中繼資料模式可以報告同一專案有可接續的活動，但不會輸出逐字稿、工具命令、路徑或工作階段 ID…
Excerpt from the skill's own SKILL.md. Read the full file on GitHub before installing: skills run with your agent's permissions.
Categories
Related skills
- planning-with-files
OthmanAdi/planning-with-files
Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
- planning-with-files
OthmanAdi/planning-with-files
Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
- planning-with-files
OthmanAdi/planning-with-files
Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
- planning-with-files
OthmanAdi/planning-with-files
Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; agent instructions read selected project planning context when invoked. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. This adapter registers no lifecycle or Stop hook, never requests continuation, and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
- planning-with-files
OthmanAdi/planning-with-files
Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
- planning-with-files
OthmanAdi/planning-with-files
Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.